Expertise determines frequency and accuracy of contributions in sequential collaboration
Many collaborative online projects such as Wikipedia and OpenStreetMap organize collaboration among their contributors sequentially. In sequential collaboration, one contributor creates an entry which is then consecutively encountered by other contributors who decide whether to adjust or maintain th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2023-01-01
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| Series: | Judgment and Decision Making |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S1930297523000037/type/journal_article |
| _version_ | 1797703620078075904 |
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| author | Maren Mayer Marcel Broß Daniel W. Heck |
| author_facet | Maren Mayer Marcel Broß Daniel W. Heck |
| author_sort | Maren Mayer |
| collection | DOAJ |
| description | Many collaborative online projects such as Wikipedia and OpenStreetMap organize collaboration among their contributors sequentially. In sequential collaboration, one contributor creates an entry which is then consecutively encountered by other contributors who decide whether to adjust or maintain the presented entry. For numeric and geographical judgments, sequential collaboration yields improved judgments over the course of a sequential chain and results in accurate final estimates. We hypothesize that these benefits emerge since contributors adjust entries according to their expertise, implying that judgments of experts have a larger impact compared with those of novices. In three preregistered studies, we measured and manipulated expertise to investigate whether expertise leads to higher change probabilities and larger improvements in judgment accuracy. Moreover, we tested whether expertise results in an increase in accuracy over the course of a sequential chain. As expected, experts adjusted entries more frequently, made larger improvements, and contributed more to the final estimates of sequential chains. Overall, our findings suggest that the high accuracy of sequential collaboration is due to an implicit weighting of judgments by expertise. |
| first_indexed | 2024-03-12T05:08:14Z |
| format | Article |
| id | doaj.art-f1e5a7399af043c981210bce14d46b7f |
| institution | Directory Open Access Journal |
| issn | 1930-2975 |
| language | English |
| last_indexed | 2024-03-12T05:08:14Z |
| publishDate | 2023-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Judgment and Decision Making |
| spelling | doaj.art-f1e5a7399af043c981210bce14d46b7f2023-09-03T08:51:30ZengCambridge University PressJudgment and Decision Making1930-29752023-01-011810.1017/jdm.2023.3Expertise determines frequency and accuracy of contributions in sequential collaborationMaren Mayer0https://orcid.org/0000-0002-6830-7768Marcel Broß1Daniel W. Heck2https://orcid.org/0000-0002-6302-9252Leibniz-Institut für Wissensmedien (Knowledge Media Research Center), Tübingen, Germany Heidelberg Academy of Sciences and Humanities, Heidelberg, GermanyDepartment of Psychology, University of Marburg, Marburg, GermanyDepartment of Psychology, University of Marburg, Marburg, GermanyMany collaborative online projects such as Wikipedia and OpenStreetMap organize collaboration among their contributors sequentially. In sequential collaboration, one contributor creates an entry which is then consecutively encountered by other contributors who decide whether to adjust or maintain the presented entry. For numeric and geographical judgments, sequential collaboration yields improved judgments over the course of a sequential chain and results in accurate final estimates. We hypothesize that these benefits emerge since contributors adjust entries according to their expertise, implying that judgments of experts have a larger impact compared with those of novices. In three preregistered studies, we measured and manipulated expertise to investigate whether expertise leads to higher change probabilities and larger improvements in judgment accuracy. Moreover, we tested whether expertise results in an increase in accuracy over the course of a sequential chain. As expected, experts adjusted entries more frequently, made larger improvements, and contributed more to the final estimates of sequential chains. Overall, our findings suggest that the high accuracy of sequential collaboration is due to an implicit weighting of judgments by expertise.https://www.cambridge.org/core/product/identifier/S1930297523000037/type/journal_articlewisdom of crowdsgroup decision-makingmass collaborationteam work |
| spellingShingle | Maren Mayer Marcel Broß Daniel W. Heck Expertise determines frequency and accuracy of contributions in sequential collaboration Judgment and Decision Making wisdom of crowds group decision-making mass collaboration team work |
| title | Expertise determines frequency and accuracy of contributions in sequential collaboration |
| title_full | Expertise determines frequency and accuracy of contributions in sequential collaboration |
| title_fullStr | Expertise determines frequency and accuracy of contributions in sequential collaboration |
| title_full_unstemmed | Expertise determines frequency and accuracy of contributions in sequential collaboration |
| title_short | Expertise determines frequency and accuracy of contributions in sequential collaboration |
| title_sort | expertise determines frequency and accuracy of contributions in sequential collaboration |
| topic | wisdom of crowds group decision-making mass collaboration team work |
| url | https://www.cambridge.org/core/product/identifier/S1930297523000037/type/journal_article |
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